TRMM Rainfall Research Articles

Assessing water storage variations in La Plata basin and sub-basins from GRACE, global models data and connection with ENSO events

ABSTRACT The La Plata basin is one of the largest river basins in the world, draining about a fifth of the South American continent. It is a system of great hydrological significance, sensitive to extreme events, and also has the most economic development in the region. This work analyses the total water storage (TWS) changes over the La Plata basin from the GRACE (Gravity Recovery And Climate Experiment) mission and compares it with TRMM (Tropical Rainfall Measuring Mission) rainfall data. Subsequently, a study was conducted on the main sub-basins for the period 2003 to 2017 to identify regional-scale variations in TWS. Precipitation and GLDAS (Global Land Data Assimilation System) runoff data was evaluated in these areas, along with their connections to ENSO (El Niño Southern Oscillation) episodes. The results suggest that the majority of TWS variations are associated with extreme climatic events, such as the 2009 drought, and are linked to ENSO periods. The statistical analysis indicates a significant correlation between severe Niño/Niña episodes and changes in TWS in the sub-basins.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Applicabilty of TRMM Precipitation for Hydrological Modeling Using HEC-HMS in Karnali River Basin in Nepal

This study aims to validate the HEC-HMS model in the Karnali river basin of Nepal, while also physically verifying the model parameters for the area. The HEC-HMS model is a semi-distributed physically based model that requires site-specific information about the weather, as well as spatial information on soil and land use. Using Arc-GIS, HEC-HMS, and HEC DSS, a hydrological rainfall runoff model was developed for the Karnali River basin at Asaraghat (DHM station 240) for the period of 1998-2005. Calibration for the time period of 1998-2001 resulted in an NSE value of 73.2% and an R2 value of 84%. Validation for the time period of 2003-2005 resulted in an NSE value of 72.4% and an R2 value of 82.02%. To evaluate the performance of the TRMM 3B42V7 rainfall product, statistical indicators such as NSE and R2 were used. Results showed that the TRMM rainfall data was reliable and had good precision in application to the Karnali River Basin. This study concludes that the TRMM rainfall data can be used as an alternative data source for basins with sparse rain gauge stations in Nepal. The results also suggest that the HEC-HMS modeling tool is good enough to relate rainfall and runoff. The model parameters for each sub-basin and reaches were analyzed with the physical characteristics of the watershed. Minor variations were found within calibrated and calculated Initial abstraction and Muskingum K, x, leading to the verification of the model parameters physically.

Investigating the Feasibility of Using Satellite Rainfall for the Integrated Prediction of Flood and Landslide Hazards over Shaanxi Province in Northwest China

Rainfall-triggered flood and landslide hazards pose significant threats to human lives and infrastructure worldwide. This study aims to evaluate the applicability of three satellite rainfall data sets—namely, CMORPH, GPM, and TRMM—for the prediction of flood and landslide hazards using a coupled hydrological-slope stability model. The spatial distribution of annual rainfall from the three satellite data sets was similar to that of gauge rainfall, with an increasing trend from the north to the south of Shaanxi Province. The average annual rainfall of CMORPH was the lowest, while that of TRMM was the highest. The modeled discharges forcing by satellite rainfall generally matched the observed discharges at four hydrological stations for the period 2010–2012, with average correlation coefficients of 0.51, 0.61, and 0.57 for the CMORPH, GPM, and TRMM rainfall, respectively. The exceedance probabilities of modeled discharges for the three satellite rainfall data sets were close to those of the observations, particularly when the discharges were low. Moreover, the landslide prediction results demonstrated that the three satellite rainfall data sets could simulate the spatial distribution of landslide events well; these simulations were consistent with the information in the landslide inventory map. Furthermore, when compared to the classical Intensity-Duration (ID) rainfall threshold method, the physically based slope stability model presented higher global accuracy under all three satellite rainfall data sets. The global accuracy of GPM rainfall was the highest among the three data sets (0.973 for GPM vs. 0.951 for CMORPH and 0.965 for TRMM), indicating that GPM rainfall provides the highest quality compared to CMORPH and TRMM rainfall. These findings provide a crucial basis for the application of satellite rainfall data in the context of flood and landslide prediction.

Time-series analysis of MODIS (LST and NDVI) and TRMM rainfall for drought assessment over India

Time-series analysis of MODIS (LST and NDVI) and TRMM rainfall for drought assessment over India

Assessment of TRMM rainfall data for flood modelling in three contrasting catchments in Java, Indonesia

Abstract This study investigates the use of the Tropical Rainfall Measurement Mission's (TRMM) rainfall data for predicting water flows and flood events in three catchments on the island of Java, Indonesia, namely, Ciliwung, Citarum and Bengawan Solo. The Shetran model was used for rainfall-runoff simulations, with rainfall input obtained from measured rain gauges (hourly and daily) and TRMM (3-hourly and daily). The daily Nash Sutcliffe Efficiency (NSE) values for the model calibration period were 0.75, 0.70 and 0.85 using rain gauge data and 0.44, 0.44 and 0.75 using the TRMM rainfall data. For the validation period, the NSE values were 0.71, 0.62 and 0.89 using rain gauge data and 0.26, 0.61 and 0.58 for the TRMM data. The Critical Success Index for predicting flooding events was improved using rain gauge data compared to using TRMM data. The results demonstrate that rain gauge data are systematically superior to TRMM rainfall data when used for simulating discharges and predicting flooding events. These findings suggest that rain gauge data are preferred for flood early warning systems in tropical rainfall regimes and that if TRMM or similar satellite rainfall data are used, the evaluated flood risks should be treated with extreme caution.

Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes.

Evaluasi Data Curah Hujan Satelit TRMM Dan GPM Terhadap Data Curah Hujan Observasi Di Kalimantan Tengah

Rainfall is an indispensable component of hydrological analysis. However, the observational rainfall data has problems such as incomplete and shorter data. Rainfall measurements using high resolution and wide area coverage satellites provide a potential alternative for various hydrological analyses. But the measurement of rainfall using satellites is not necessarily accurate due to the difference in resolution of the observational rainfall data, especially in Central Kalimantan Province. This study aims to determine the accuracy of satellite rainfall data based on observational rain data and evaluate it. Evaluation is done by looking for the relationship between the two-rainfall data using a regression model. The results showed that the TRMM total grid rainfall data (R2 = 0.5233) had a higher correlation to the observational rainfall data compared to the GPM rainfall data (R2 = 0.3909). Furthermore, a discharge analysis was carried out in one of the watersheds in Central Kalimantan, namely the Kahayan watershed using the Nakayasu Hydrograph Synthesis Unit method with a certain return period with satellite rainfall data input which has a fairly good accuracy based on the results of the analysis. The estimated discharge with return periods of 2, 5, 10, 25, and 50 years for TRMM rainfall data before correction are 1976.74m3/s, 2165.66 m3/s, 2239.19 m3/s, 2313.63 m3/s, 2375.69 m3/s respectively, and for the estimated discharge after correction are 2549.35 m3/s, 2670.59 m3/s, 2736.42 m3/s, 2801.37 m3/s, 2854.82 m3/s.

Analysis The Influence of Hydrometeorological Disaster in Kera River Wajo Regency

Recently, Indonesia has been dominated by hydrometeorological disasters which include landslides, tornadoes, droughts and floods. Hydrometeorological disasters cannot be separated from the consequences of climate change that occur. The purpose of this study is to examine the influence of climate change on hydrometeorological disasters in the Kera River of Wajo Regency. This research uses quantitative methods. The data used are primary data in the form of field surveys and secondary data in the form of river topographic data and TRMM rainfall. The climate change greatly affects the increase in rainfall in the watershed, and flooding in the Watershed. Changes in rain characteristics in the watershed, in the form of annual rain height and maximum daily rain tend to increase, resulting in flooding around the Kera River area. The highest discharge that occurs at 2 year return period is 147.1 m3/s, 5 year return period is 212.5 m3/s, 10 year return period is 251.3 m3/s, 25 year return period is 296.1 m3/s. Therefore, it is necessary to carry out flood disaster mitigation with structural methods shown, by normalizing rivers, building flood embankments respectively. The results of this study found that climate change that occurred in the Kera River had an effect on the number of hydrometeorological disaster events, especially floods.

Response of Rangeland Vegetation to Recent Trends in Seasonal Climate in Mansehra, Pakistan

The deterioration of rangeland resources as a result of environmental changes is a serious concern in the Himalayan mountainous region of Pakistan. The present study is aimed to evaluate the response of vegetation cover of rangeland to recent trends in climate parameters, such as the seasonal temperature and rainfall in the Mansehra district of Khyber Pakhtunkhwa province, Pakistan. Correlation analysis was performed between the MODIS data products, i.e., NDVI (Normalized difference vegetation index) and LST (Land surface temperature), and TRMM rainfall datasets of the 2000-2018 period. NDVI indicated a negative correlation with LST of winter (R= -0.56), spring (R= -0.7), summer (R= -0.24), and autumn (R= -0.23) significant (p<0.05) for winter and spring seasons only. In contrast, the correlation of NDVI was observed positive with seasonal rainfall exhibiting coefficient of correlation values of 0.41, 0.79, 0.64, 0.7 for winter, spring, summer, and autumn significant (p<0.05) for the last two seasons only. The low correlation observed between NDVI and LST of summer and autumn seasons is likely because of the prevailing stress condition of chlorophyll contents of the vegetation cover under warming conditions. However, this situation appears to be compensated by the rainfall as indicative of the moderate to strong correlation between the NDVI and rainfall of these two seasons. The least NDVI values observed during the winter season indicate limited vegetation cover for grazing opportunities in the lower valleys. However, an in-depth investigation of production patterns would further facilitate analyzing the grazing potential to support decision-making for long-term grazing management.

Correlation of BMKG with TRMM for daily and monthly rainfall series in Banten region

Daily and monthly rainfall data series are necessary data for planning purposes in Civil Engineering and other fields. Incomplete rainfall data often occurs, so that rainfall data must be estimated based on rainfall data from several other nearby locations. The addition of rainfall data can lead to inaccurate planning.
 Rainfall data used for planning in the civil engineering sector is usually taken from the BMKG station. This data is taken from the rainfall station above the ground. Besides, that can also produce rainfall data from TRMM. Can take rainfall data from TRMM at all locations according to a coordinate of location. This rainfall data denotes an average rainfall taken from the satellite approximately 250 meters above the ground surface.
 An equation will be obtained by comparing the daily and monthly rainfall data from the two data sources. Based on TRMM rainfall, we can use the equation to estimate ground rainfall in a location.
 In this study, daily rainfall, monthly rainfall, the spectrum of daily and monthly rainfall data from BMKG are compared with rainfall and the spectrum of daily and monthly rainfall data from TRMM. The analysis results show that the monthly rainfall data from TRMM and BMKG correlate better than daily rainfall data.

RAIN DETECTING ACCURACY OF WEATHER RESEARCH FORECASTING (WRF) AND TRMM RAINFALL PRODUCT OVER CAMBODIA

Rainfall is one of the important parameters for evaluating flood hazard risk. Cambodia is a vulnerable country to extreme rainfall where the number of rain gauges over the country is limited. Therefore, the possibilities of applying rainfall products from satellite observation and rainfall forecasting models are crucial for the country. The purpose of this research is to evaluate the detecting accuracy of the rainfall-based Weather Research Forecasting (WRF) model and TRMM rainfall products by comparing with observed rainfall during heavy rainfall for different topography over Cambodia. The categorical statistic is used to calibrate the rainfall from the WRF model with observed rainfall from 23 stations over Cambodia on selected heavy rainfall dates of 15, 17, and 19 September 2019. Cambodia experienced floods along the Tonle Sap River and the Mekong Basin by the triggered heavy rainfall. The results show that the detecting accuracy of days 15, 17, and 19 from TRMM rainfall matched with observed rainfall are 55%, 71%, and 63%, respectively. The average detecting accuracy of mountainous is 65% whereas plains are 63.33%. The average detecting accuracy of coastal and Tonle Sap is 53.66% and 63%, respectively. Moreover, the detecting accuracy of days 15, 17, and 19 forecasts from the WRF model compared with observed rainfall are 41%, 69%, and 63%, respectively. The average detecting accuracy of mountainous, plains, coastal, and Tonle Sap are 52%, 55.66%, 52.33%, and 65.66%, individually. The forecast rainfall from the WRF model and TRMM could detect the rainfall. They are therefore should be used in the areas that lack rainfall stations in Cambodia.

Response of enhanced vegetation index changes to latent/sensible heat flux and precipitation over Pakistan using remote sensing.

For a sustainable development and ecological integrity, it is of worth importance to monitor land use/ land cover (LULC) changes and related land-atmosphere fluxes. To serve this purpose, we have used moderate resolution imaging spectroradiometer (MODIS) retrieved-enhanced vegetation index (EVI), MERRA-2 re-analysis surface heat fluxes (latent heat flux, sensible heat flux and specific humidity), TRMM rainfall data, and OMI retrieved aerosol index (AI) over Pakistan during 2000 to 2021. High EVI (0.66) is observed in May 2021 as compared to May 2000 over Muzaffarabad, Srinagar, north and northwest of Khyber Pakhtunkhwa, east of Punjab and along the Indus River in Sindh. The highest increase in vegetative area is observed in Baluchistan (~ 366%), followed by Manavadar (~ 60%), Khyber Pakhtunkhwa (~ 41%), Sindh (~ 37%), and Punjab (~ 20%) whereas Gilgit-Baltistan and Jammu and Kashmir show reduction in vegetative area by 21% and 11% respectively. The coefficient of determination (R2) is found to be highest between rainfall and latent heat flux (R2 = 0.59) followed by rainfall and specific humidity (R2 = 0.35), and rainfall and sensible heat flux (R2 = 0.06). The latent heat flux shows increasing trend at the rate of 0.003 Wm-2 winter-1, 0.0065 Wm-2 pre-monsoon-1 and 0.0272 Wm-2 post-monsoon-1 during 1980-2021 whereas sensible heat flux shows decreasing trend at the rate of 0.00056 Wm-2 winter-1, 0.00249 Wm-2 pre-monsoon-1 and 0.0037 Wm-2 post-monsoon-1 during 1980-2021. Specific humidity depicts increasing trend at the rate of 0.0002 Wm-2 winter-1, 0.0038 Wm-2 pre-monsoon-1 and decreasing trend at the rate of 0.0080 Wm-2 post-monsoon-1 during 1980-2021. The interannual variations in AI show highest AI of 2.28 in 2021 with maximum positive percentage anomaly of 28.06% during 2007.

Spatiotemporal patterns of agricultural and meteorological droughts using SPI and MODIS-based estimates over a Brazilian semiarid region: study case of Upper Paraíba River basin

Drought is a very complex natural phenomenon that affects the water supply and consequently impacts agricultural and socioeconomic activities, causing social and political crises. The objective of this study is to spatiotemporally analyze meteorological and agricultural droughts based on multiple indices in the Upper Paraíba River basin for the 2001–2017 period. The agricultural droughts were analyzed using four drought indices (NDVI, LST, VSWI, and VHI) based on data from the MODIS sensor on the TERRA and AQUA satellites, while the meteorological droughts were characterized using the standardized precipitation index (SPI) at three different time scales based on TRMM rainfall data. The values of LST, NDVI, VHI, VSWI, and SPI revealed how much vegetation is influenced by rainfall variability in this semiarid region of Brazil. The estimated results of VSWI and VHI show the years with extremely wet and dry conditions from spatial and temporal perspectives. The relationship between estimated drought indices and observed relative humidity showed that VSWI (R2 = 0.78) was best fitted when compared to VHI (R2 = 0.46). Thus, it can be concluded that the VSWI index is better adapted because it presented more consistent results for the behavior of agricultural droughts when compared to the VHI. The results of the SPI analysis were used for the identification of the main drought events that affected the study basin. This combined technique of different drought indices easily produced an alternative method for comprehensive drought assessment.

The ratio of BMKG and TRMM Rainfall Data in West Java Province by Using Statistical Parameter and Correlation Analysis

Weather observations can be done in two ways, namely weather observations based on weather stations and based on remote sensing such as satellites. One of these weather study data is rainfall measured from the BMKG rain observation post and TRMM satellite observations. To see the pattern of the distribution of rain that has occurred, the two weather observations can be connected as a reference for the distribution of rain. The purpose of this study was to analyze the correlation value of rainfall data between BMKG and TRMM by looking at the comparison graph and analyzing the comparison of statistical parameters. This research was conducted using daily rainfall data from 1998-2018 at four rain stations in West Java Province and taking descriptive decisions in the form of pictures and graphs in the form of daily, monthly and annual data. Based on the analysis results, the largest correlation value is in the annual cumulative with a value of 0,88-0,94, the smaller the number of days, the smaller the correlation value. The BMKG and TRMM rainfall data have relatively the same pattern, but the maximum data have differences so that it reduces the correlation value. In the Statistical Parameter analysis, it can be stated that the difference in the values ??of the statistical parameters is directly proportional to the daily cumulative, that the difference in the median and mean values ??in the BMKG and TRMM data is greater in the data with a larger cumulative. Meanwhile, for linear regression analysis, it was found that the greatest value was the cumulative 1-year value with a coefficient of determination from 0,78 - 0,89.

Statistical Relationships between Two Types of Heavy Rainfall and Low-Level Jets in South China

AbstractTwo types of heavy rainfall, namely warm-sector and frontal heavy rainfall, coexist in South China during the presummer rainy season and manifest as varying mechanisms and features. They both exhibit close relationships with two types of low-level jets (LLJs): the boundary layer jet (BLJ) and synoptic-system-related low-level jet (SLLJ), but in different ways. The motivation of the present study is to elucidate the statistical relations between the two types of heavy rainfall and LLJs over South China using TRMM rainfall data and ERA5 reanalysis. Generally, warm-sector heavy rainfall mainly occurs over coastal areas and during the early morning, which is primarily caused by the interaction between the nocturnal BLJ and land breeze. In contrast, frontal heavy rainfall is mostly concentrated in inland regions and modulated by distinct diurnal forcings at different locations. Statistical analysis indicates that 76% (62%) of the warm-sector (frontal) heavy rainfall events are associated with LLJs. In the presence of heavy rainfall, low-level winds are often strengthened over Beibu Gulf, northern South China Sea, and the south side of fronts, corresponding to two branches of southerly BLJs at ~950 hPa over the ocean and the southwesterly SLLJs at ~850–700 hPa on land, respectively. Furthermore, BLJs are shown to be linked to both types of heavy rainfall and with the most frequent occurrences of rainfall in their exit region, whereas SLLJs are more closely associated with frontal heavy rainfall. The left side (entrance) of the SLLJ axis is favorable for frontal (warm-sector) heavy rainfall production. The regional rainfall distributions are affected by the structures and locations of LLJs.

Flood Hazard Mapping and Assessment of Precipitation Monitoring System Using GIS-Based Morphometric Analysis and TRMM Data: A Case Study of the Wadi Qena Watershed, Egypt

Wadi Qena is one of the Nile Valley areas particularly at risk of severe flash flooding, located in Egypt. The study aims to verify TRMM rainfall data (TRMM 3B42), using eight stations across Egypt as well as relies on morphometric analysis to generate a flood risk map based on the ranking method. Three process could be recognized through the study, calibration, correction and verification processes. The results discuss the match daily rainfall trends of TRMM and observed data, producing a correction equation for TRMM data with root mean square error (RMSE) value of 0.837 mm d-1 and R2= 0.238 (calibration process). On the other hand, a verification process, using the developed correction equation, obtain RMSE value of 1.701 mm d-1 and R2= 0.601. The morphometric analysis shows 32 sub-basins with a hazard degree from moderate to high, amounting to 50.3% of the watershed area. Conclusively, this study confirms that the current monitoring system is not enough to cover the whole area, especially the high-risk sub-basins, and TRMM data could provide key information for water-related applications in Egypt.

Rainfall-runoff simulation using satellite rainfall in a scarce data catchment

This study investigates the performance of TRMM and PERSIANN satellite rainfall data as input in a reliable rainfall-runoff model in order to provide information to the flood early warning for the Voshmgir Dam, Iran. Calibration of both continuous daily rainfall-runoff and an event-based flood was done using HEC-HMS Nelder-Mead (NM) method. Furthermore, simulations based on daily versus 3-hourly TRMM were compared to evaluate the effect of input time-step of rainfall-runoff model. Results show that the deficit and constant loss method is able to successfully predict the observed runoff. In addition, the Green-Ampt method using 3-hourly TRMM data showed a good capability to simulate daily peak discharges. The current study demonstrates the suitability of HEC-HMS for continuous and event-based runoff simulation in a complex watershed. Therefore, this research will have a significant contribution to the future development of water resources planning in this catchment in particular and in other data-scarce catchments.

Groundwater depth prediction using Shetran model in Citarum River basin

Drought is a natural disaster that lasts a long time and has an impact on various things that can seriously affect human life, economy, agricultural production, and also ecological environment. In Indonesia, floods and droughts become annual disasters that often occur and are difficult to avoid, even in big cities. Drought cannot be considered a trivial problem because its impact is very detrimental to every aspect of life. Drought can be seen from several indicators. The depth of groundwater can be an indicator of drought. The purpose of this study was to predict the depth of groundwater in the Citarum River Basin. This study uses a spatially distributed Shetran model. The Shetran model was developed by the School of Civil Engineering and Geosciences at Newcastle upon Tyne University. The hydro-climatological data from satellite measurements during 2001 to 2017 were used in Shetran model. Input data in this study consisted of digital elevation models, TRMM rainfall data, evaporation data, soil properties, and land-use change land cover change data. The results showed that within 17 years the land use types in the Citarum watershed had undergone significant changes. This land use change affects the depth of groundwater in the Citarum watershed.

Estimating of soil moisture using shetran model at Cisanggarung catchment area

Soil moisture is one of the essential controls for the hydro-climatology processes. Soil moisture value can be used as an indicator of soil fertility so that it has a vital role in increasing agricultural and plantation production. Understanding the soil moisture variability on a spatial-temporal scale is very interesting in many practical applications such as drought prediction and agricultural modelling. The purpose of this study was to estimate the soil moisture value which distributed in space and time in the Cisanggarung River Basin with the spatial resolution of 500 m × 500 m and daily temporal resolution. The Shetran physical-based hydrological model was applied by utilizing hydro-climatological data derived from the remote sensing measurements from 2001 to 2017. The Shetran model input data consisted of the digital elevation model, land use land cover, TRMM rainfall data, evaporation data, and soil properties data. The results of this study indicate that Shetran hydrological model is reliable to estimate soil moisture in a watershed. This soil moisture values are then validated with the historical data of drought disaster.