Land use change analysis and the impact on the streamflow of the Keureuto River

The increasing of population is proportional to the natural resource’s exploitation. Over the past few decades, the activity has converted the natural land into agricultural and plantation fields, urbanized areas, and other types of use. In some watershed systems, land use conversion has reduced the system’s capability both in sustaining water resources and in preventing extreme runoff. Such is the case of the 302 km2 Krueng Keureuto Watershed which supports the water supply for the North Aceh District, both for irrigation and municipal use. Therefore, this study aims to investigate the land use change in the system and its impact on the streamflow. The land use is classified based on the Landsat satellite images of the year 2015 and 2021, using the Supervised Classification tool in ArcGIS. The impact is investigated using a Hydrologic Modelling System (HEC-HMS). The outlet is defined based on the Automatic Water Level Recording (AWLR) station downstream of the Keureuto River. A significant reduction of the forest is estimated at 27.67% in seven years. In contrast, the land use types such as urbanized areas, agricultural fields, and shrubs are increasing to 6.34%; 4.75%; and 5.75%, respectively. Based on the model simulation it is confirmed that the changes in land use have altered the river flow regime.


Introduction
Land serves as a crucial part of fulfilling human needs as a medium for planting in agricultural activities, building urban, and for other uses.Its significance varies individually, contingent upon differing viewpoints and interests.Changes in land use have a significant impact particularly on the hydrological processes which related to watershed servis on water resources [1].Rainfall and land use conversion are one of the main factors of groundwater recharge which will affect groundwater level fluctuations over time [2].Because of these problems, it is necessary to monitor the changes in land use within the Krueng Keureuto Watershed.Monitoring land use is necessary to track land transitions that change due to human activity and use.In natural resources management and monitoring environmental changes, the current status of land use change plays an important role, exerting a profound influence on both human and physical conditions [3].Recently, the Keureuto River has caused flooding within its catchment area, especially in the surrounding areas, such as the city of Lhoksukon.This disrupts community activities and paralyzes the economy, leading to substantial losses to the community and region.To address this issue, a range of strategies can be employed, spanning both structural and non-structural approaches.Structural endeavors encompass the construction of drainage and reservoirs.However, these endeavors entail significant expenses, resulting in significant costs that must be spent.Therefore, non-structural approaches are indispensable, offering cost-effective alternatives through a comprehensive analysis of optimizing land use within the watershed.A modeling approach may be implemented to address the analysis.Developments in geographic information system (GIS) and remote sensing (RS) technologies are significant tools in providing and processing spatial data for watershed modeling.The utilization of satellite remote sensing images as data sources has greatly facilitated the extraction, analysis, and simulation of information related to land use and land cover [4].Therefore, the aim of this study is to investigate the land use change in the system and its impact to the streamflow of the Keureuto River.

Data and Methodology
The data used in this study consists of the Digital Elevation Model (DEM), Global Precipitation Measurement (GPM) daily rainfall data year 2015 to 2021, and daily water level measurements at the Krueng Keureuto Automatic Water Level Recorder (AWLR).Land use data for the years 2015 and 2021 is a product of the Landsat 8 satellite images classification using a supervised classification tool in ArcGIS.Soil-type data were obtained from the Geospatial Information Agency (BIG).The simulation is carried out utilizing the HEC-HMS software, which was developed by the Center for Hydrological Engineering in collaboration with the US Army Corps of Engineers.This software has been widely employed in various hydrological research endeavors and is designed to replicate rainfallrunoff processes in diverse geographical areas, encompassing floods, water resources, and both small and large river basins, as well as urban and natural runoff from watersheds [6].Applying the SCS Unit Hydrograph method, this study aims to establish a model of watershed characteristics, including soil type, vegetation cover, and land usage, and the corresponding runoff curve numbers.These numbers serve as indicators of the potential runoff that can be expected during specific rainfall events [7].This method analyzes discharge fluctuations with different land use relationships.

Results and Discussions
Landsat 8 raster data were processed to identify land use types.Several land use classifications were obtained, including forests, agriculture, urban, plantations, rice fields, shrubs, and rivers.Furthermore, the extents of each land cover type were determined using overlay techniques within the ArcGIS software.The products of the land use mapping within the Krueng Keureuto Watershed are illustrated in Figure 2. Based on the land use analysis of Figure 2, it can be visually inferred significant changes in land use between 2015 and 2021.The changes, provided in Table 1, over the seven years, are increasing areas of agriculture, urban, and plantation; and decreasing areas of forest and rice fields.The reduction area of forest and rice fields is 27.67% and 1.21% respectively.The increasing area of agriculture, urban, and plantation are 4.75%, 6.34%, and 11.83% respectively.These conversion may affect erosion [8] and reduces the capacity of the soil to absorb water which increase the surface flow.Based on the FAO soil map data derived from the official website of the Indonesian Geospatial Agency (BIG), the Krueng Keureuto Watershed is divided into four types of soil which are two types of Orthic Acrisols (Ao), Dystric Fluvisols (Jd) and Chromic Fluvisols (Lc), presented in Figure 3. Thus, the soil-type groups are converted to the hydrologic soil group (HSG), shown in Table 2  In this study, three models are implemented which are loss, transform, and routing.The SCS CN method is used for the loss model in calculating the rainfall loss due to evaporation, interception, infiltration, and runoff.This method was selected based on less data requirement [10] considering lack of data availability for the study area.The SCS unit hydrograph is of the transfer methods provided by the model.It simulates the process of the direct runoff based on precipitation excess at surface and transforms the excess into point runoff [11].The parameter used in the routing model is lag time which highly influences the peak and the times of the peak outlet at the outlet [12].
The simulation is conducted and compared to the observed flow at the outlet through calibration.This calibration process is expected to be able to determine the parameter values of the parameters such as the Curve Number (CN) value, initial absorption, and area of imperviousness [13].The calibration can be performed using the Nash method, where the square of the difference between the simulated and observed discharges is compared with the square of the difference between the observed discharge and the average observed discharge.The NSE (Nash-Sutcliffe Model Efficiency) is applied to measure the efficiency of the hydrological model [14].The NSE formula has a level of model accuracy based on the value of the Nash-Sutcliffe Efficiency coefficient [15].The efficiency criteria, presented in Table 3, aimed to evaluate the validity of the model.The calibrated parameters are listed in Table 4.The simulation result is overlaid with the observed hydrograph.Figure 4 shows the graph for the year 2015, and Figure 5 for the year 2021.Based on the plot, it can be visually interpreted that the simulation results of the year 2015 show a closer trend to the observed compared to that of the year 2021.It is also confirmed by the model validity criteria NSE, which returned 0.57 and 0.37 for the year 2015 and 2021, respectively.The trend of the model results shows that the peak time and flow fluctuation are captured, but the magnitude of peaks is underestimated.Plotting simulation results and observed flow using Flow Direction Curve (FDC), as shown in Figure 6, the curve of the year 2021, both of the simulated and observed flow, is steeper than that of the year 2015, confirming that the surface runoff is higher in the year 2021 than that of the year 2015.This indicates that the land use change impacts the flow.Moreover, the increasing value of CN composite in the model from 75.67 of the year 2015 to 82.19 of the year 2021 also confirms a continuous reduction in water absorption indicating the land becoming impermeable to water.This is due to the decrease in the water-carrying capacity of the watershed and greater surface runoff.

Conclusions
Based on the land use analysis of Landsat image data of the year 2015 and 2021, does confirm that the change in land use within the Krueng Keureuto Watershed system is significant.Although the model application for the system does not particularly answer the impact of land use on the streamflow, the HEC-HMS model for the watershed system is acceptable for the year 2015 and fairly acceptable for the year 2021.Through FDC however, it can be implied of the impact that the results do confirm the increasing of surface runoff from the year 2015 to 2021.This study should initiate further research on the modeling approach in the system by introducing a more reliable data set, such as rainfall ground data.

Figure 1 .
Figure 1.The study area of the Krueng Keureuto Watershed

Figure 2 .
Figure 2. Krueng Keureuto Watershed Land Use Map of the year 2015 (a) and year 2021 (b) . The land use and soil map are considered to derive Curve Number (CN) values in the watershed.

Figure 4 .
Figure 4. Simulated and observed the flow of the year 2015

Figure 5 .
Figure 5. Simulated and observed the flow of the year 2015

Figure 6 .
Figure 6.Flow Direction Curve of the year 2015 and year 2021