Estimation Of Water Resources Research Articles

Rainfall-Runoff Estimation in the North-Eastern Region of Bangladesh Using SCS-CN Method

Measuring discharge in a developing nation like Bangladesh is very important for flood prediction, land management, and sustainable development. Rainfall-induced runoff is a part of the cycle of hydrology and is required for efficient water resource planning. The most significant debatable procedure in hydrology is calculating and determining catchment surface runoff. The study aims to estimate runoff using the GIS-based Soil Conservation Service-Curve Number method (SCS – CN) method, where this hydrological model has a physical foundation and spatial distribution, and the curve number plays a key role in this model's runoff computation. The Hydrologic Soil Group (HSG) present in the study area and the land use pattern are used to find the Curve Number (CN). The calculated weighted CN for Antecedent Moisture Condition (AMC) I, II, and III for the study area was roughly 74.78, 87.12, and 94.06 respectively with the incorporation of Land use and land cover (LULC) and HSG. For total average rainfall of 35163.8mm, the SCS-CN approach determined the total average runoff 16077.55 mm for the period of 2009-2018. With a correlation coefficient of 0.966, the average rainfall and SCS-CN discharge have a significant linear relationship. The runoff regulates the amount of water that enters stream systems and returns extra precipitation to the ocean benefitting the hydrological cycle. With this runoff estimation, the quality and amount of water resources can be better understood, managed, and tracked. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(2), 2023, P 83-96

Assessment of snow simulation using Noah-MP land surface model forced by various precipitation sources in the Central Tianshan Mountains, Central Asia

Accurate mountainous snow estimation is paramount for hydrological processes and water resources estimation in arid regions. Using Land surface models (LSMs) is a practical numerical approach for snow estimation in a complex orography region such as the Tianshan Mountains. However, the bias of precipitation forcing is a major source of uncertainty for snow simulation over scarce-data regions. This study evaluated the performance of 4 km snow simulations using the Noah-MP LSM driven by eight precipitation sources. They are retrieved from non-model products and climate model simulations during the 2018-2019 cold season in the Central Tianshan Mountains (CTS) region. Multi-source validation datasets (in-situ observation, field snow pits, and remote sensing products) have been used for evaluation and uncertainty estimation. The results showed that the difference in precipitation amount significantly affected the snowpack simulation performance. Compared with non-model products (Global Land Data Assimilation System (GLDAS), Global Precipitation Measurement (GPM), and Gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP)), the cold season precipitation from climate model simulations exhibited a better performance overall in the high-elevation regions (elevation > 1000 m) evaluated by in-situ observations. In addition, the 4 km convection-permitting modeling (CPM) precipitation (WRF-Morrison and WRF-WSM6) showed higher accuracy (RMSE: 23.48 mm/season and 18.94 mm/season, respectively) than gray-zone resolution simulations and ERA5-land driven runs in the high-elevation regions. The snow depth simulation driven by WRF-Morrison precipitation had the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day) value in the high-elevation regions compared with in-situ observation. Meanwhile, CPM precipitation-driven runs exhibited the smallest RMSE value based on the assessment of snow field pits. In addition, the CPM-driven simulation achieved the closest match to the elevation-based distribution of snow cover days in regions with elevation over 1000 m and duration over 60 days compared to the MODIS product. The findings of this study highlight that CPM with proper parameterization configurations has added values in producing realistic topographic precipitation for snow modeling using LSMs over data-scarcity mountainous areas.

Enhancing Model Calibration and Uncertainty Estimation through Multi-Objective Optimization with SUFI-2 Algorithm

The research aimed to quantify the uncertainty in hydrological modeling results, especially in data-scarce areas like the Teesta River. The developed grid-based model from Texas Agricultural and Management University (TAMU) with the help of simulation plugin Quantum Soil and Water Assessment Tools (QSWAT) and calibration (CAL) & validation (VAL) using SWAT-Calibration Uncertainty Program (CUP). Multiple algorithms Minimize & Maximize (MIN & MAX), specifically with Sequential Uncertainty Fitting Version-2 (SUFI-2) algorithm (Best fitted simulation), were used as the popular for the many ideal simulations as possible that are within 95% of the true predictions. The research investigated how eleven different objective functions (OBJF) influenced the CAL results, parameterization, and surface water estimation for the stream flow of the Teesta River. While several OBJF performed well, eight of them, namely Chi-square (Chi2), Multiplicative (Multi), Modified Nash-Sutcliffe efficiency factor (MNSE), Nash-Sutcliffe efficiency (NSE), summation (SUM), Coefficient of determination (R2), Modified coefficient of determination (bR2), and Relative Squared Error (RSR), consistently yielded very similar results because of mostly used MIN methods. For instance, NSE resulted in a CAL & VAL score of 0.85 and 0.88, while R2, MNSE, and bR2 had scores of 0.88 and 0.89 during both CAL & VAL periods. All results from NSE, R2 and bR2 show a correlation between 0.84 to 0.90. However, other OBJF provided slightly different sensitivity values. The Percentage Bias (PBIAS) scored 0.76 for CAL and 0.77 for VAL. The Kling-Gupta efficiency (KGE) scored 0.79 during both the CAL & VAL periods. Additionally, the sum of squares (SSQR) obtained shallow values for both CAL & VAL processes. The aforementioned research shows that varied OBJF can affect CAL findings and hydrological modelling parameter values, emphasizing the need to use several OBJF in uncertainty analysis for more accurate water resource estimation the calibrated model and its outputs aid hydrologists and water resource administrators in agricultural and water assessment. They also support research on climate change's effects on water supply and quality, droughts, and food. The unique idea and flexible techniques make them suitable for most countries.

Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations

This study carefully assesses the capability of supervised machine learning classification algorithms in identifying land cover (LC) in the context of the Jhelum River basin in Kashmir. Sentinel 2 and Landsat 8 high-resolution data from two satellite sources were used. Through preprocessing techniques, we removed any potential noise inherent to satellite imagery and assured data consistency. The study then utilized and compared the skills of the supervised algorithms random forest (RF) and support vector machine (SVM). A hybrid approach, amalgamating classifications from both methods, was also tested for potential synergistic enhancements in accuracy. Using a stratified random sampling approach for validation, the SVM algorithm emerged with a commendable accuracy rate of 82.5%. Using simulations from 2000 to 2015, the soil and water assessment tool (SWAT) model was used to further explore the hydrological effects of LC alterations. Between 2009 and 2019, there were discernible changes in the land cover, with a greater emphasis on ranges, forests, and agricultural plains. When these changes were combined with the results of the hydrologic simulation, a resultant fall in average annual runoff—from above 700 mm to below 600 mm—was seen. With runoff values possibly ranging between 547 mm and 747 mm, the statistics emphasize the direct effects of urban communities encroaching upon forest, agricultural, and barren lands. This study concludes by highlighting the crucial role that technical pipelines play in enhancing LC classifications and by providing suggestions for future water resource estimation and hydrological impact evaluations.

Water Flow Analysis of the Kayanga-Anambé-Lake Waïma Hydrological Complex by Flow-flow Correlation and Rainfall-flow Modeling Approaches

This article presents an assessment of the surface water resources of the Kayanga-Anambé hydrological complex, located in West Africa. This hydrological complex, one of the main characteristics of which is exclusively agricultural and pastoral, is subject to a changing climate and strong anthropogenic pressures leading to resource use conflicts. Therefore, a better knowledge of water availability is essential to guide policies for the management and adaptation of this geosystem. Two approaches are used to estimate the contributions from the different sub-watersheds of the Kayanga: (i) a correlation between the flow rates of Wassadou and those of the other hydrometric stations of the Management and Planning Unit (UGP) of Casamance and eastern Senegal – in particular for filling the gaps observed in the data series from the Wassadou, Niandouba and Vélingara Pakane stations – and (ii) the implementation of a rainfall-runoff modeling approach at monthly time intervals, with the GR2M model, to reconstruct seasonal flows and contributions from the Kayanga-Anambé-Lake Waïma complex. The results show correlation coefficients which vary from 0.24 to 0.96 depending on the stations, showing a strong relationship between the flow rates of the Kayanga at Wassadou and those of the other stations. The maximum correlation coefficient is noted at the Mako station (0.96), on the Gambia river. Furthermore, a Nash of 0.86 was obtained with the GR2M model. Thus, the parameters X1 and X2 of the model were used to reconstruct the seasonal flows and contributions from the Kayanga-Anambé-Lac Waïma complex. This made it possible to have long enough time series of flow rates for a better estimation of water resources and their temporal fluctuation.

A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region

Semi-arid and arid systems cover one third of the earth’s land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product (94 % producer’s accuracy (PA) vs 84 %) and comparable user’s accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments.

Taquari Megafan: Estimation of Water Resources in the Pantanal Wetland, Brazil

Taquari Megafan: Estimation of Water Resources in the Pantanal Wetland, Brazil

Estimation of Groundwater Recharge and Draft of Arwal District Watershed based on Changing Trend of Rainfall and Population Forecasting

Increasing demand of fresh water is not only an issue in India but across the world. Rapid growth of population is a major cause of harassing the natural resources and groundwater is one of them. The methodology for groundwater resources assessment in India is mainly based on Ground Water Resources Estimation Committee,1997 and it involves assessment of annual groundwater resources recharge, annual groundwater draft (utilization) and the percentage of utilization with respect to recharge (stage of development). The assessment units (blocks/watersheds) are categorized based on stage of groundwater development (utilization) and the long term water level trend. This study estimates the recharge and draft of Groundwater in Arwal District due to uses for irrigation, domestic and industrial uses, etc. The characteristic of annual rainfall, monsoon rainfall and non-monsoon rainfall were analyzed. The average annual rainfall of Arwal district is 743.05 mm, monsoon rainfall as 654.18 mm and non-monsoon rainfall was recorded as 66.87 mm. Groundwater recharge was estimated at 49766.15 ha-m and draft was determined as 52973.15 ha-m. Population study of district was forecasted using Geographical Increased method. Based on this study results, the total annual demand of water for Domestic purposes was estimated at 41.09 MCM. Draft of groundwater was observed more than recharge of groundwater. This needs proper planning and management of existing groundwater resources.

Design of Water Distribution System for Thirumitta code Grama Panchayat

The aim of this project is to supply the water to client with acceptable grade and amount. Jal Jeevan Mission (JJM) has been launched that aims at providing practical functional household tap connection (FHTC) to each village unit by 2025 in Republic of India. This project aims on designing a water distribution system for the Thirumittacode gramma panchayath situated in Pattambi Municipality, Palakkad district, Kerala. This project revolves around population prediction of the general practitioner by 2024 and 2054, water demand calculations, planning the water distribution system with associate degree acceptable water resource and value estimation. Use of loop 4 software system adds on to the advancement of this project.

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

Groundwater Resource Assessment of an alluvial aquifer in parts of Varanasi and Sant Ravidas Nagar Districts, Uttar Pradesh, India using GRE-2015

The importance of groundwater in India can be realized from the fact that the 85% of rural drinking water supply is dependent on groundwater. There has been paradigm shift from surface to ground water in view of poor quality and reduction in river flow/discharge in the recent past. The concept of micro and sustainable irrigation may be achieved efficiently through groundwater. Therefore, the groundwater resource assessment is essential to make the development plan for meeting out the drinking and irrigation demand for any groundwater unit. An attempt has been made to evaluate the groundwater resource using the latest Ground Water Resource Estimation (GRE-15) of an area that has gone through extensive irrigation using groundwater so that the groundwater stress areas can be sustainably managed. The present research is based on the Groundwater Resource Estimation methodology- 2015 to evaluate the groundwater availability and the stress conditions occurring in the study area. The stage of groundwater development inferred after the study shows that Araziline block of Varanasi and Bhadohi block of Sant Ravidas Nagar district of Uttar Pradesh are falling under the critical category while Sevapuri block of Varanasi district of Uttar Pradesh falls under the semi-critical category. The current estimation warrants the immediate need for proper management of groundwater resources.

Water level status of Indian reservoirs: A synoptic view from altimeter observations

Abstract Most of the part of India is already under water-stressed condition. In this regard, the continuous monitoring of the water levels (WL) and storage capacity of reservoirs, lakes, and rivers is very important for the estimation and utilization of water resources effectively. The long term ground observed WL of many of the water bodies is not easily available, which may be very critical for proper water resources management. Satellite radar altimetry is the remote sensing technique, which is being used to study sea surface height for the last 25 years. The advancement in radar technology with time has provided the opportunity to exploit the technique to retrieve the WL of inland water bodies. In the current study, an attempt has been made to generate long term time series on WL of around 29 geometrically complicated inland water bodies in India. These water bodies are mainly large reservoirs namely Ban Sagar, Balimela, Bargi, Bhakra, Gandhi Sagar, Hasdeo, Indravati, Jalaput, Kadana, Kolab, Mahi Bajaj, Maithon, Massanjore, Pong, Ramganga, Ranapratap Sagar, Rihand, Sardar Sarovar, Shivaji Sagar, Tilaiya, Ujjani, and Ukai. The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. Further, an attempt has also been made to estimate the WL of gauged/ungauged lakes namely Mansarovar, Pangong, Chilika, Bhopal, and Rann of Kutch over which Saral-AltiKa pass was there. As after July 2016, the SARAL-AltiKa is operating in the drifting orbit, systematic repeated observation of WL data of all reservoirs was not possible. The data of drifted tracks of Saral-AltiKa were tested for WL estimation of Ban Sagar reservoir. As the ERS-2, ENVISAT RA-2 and Saral-AltiKa all were having almost the same passing tracks, a long term WL series of these lakes could be generated from 1997 - 2016. However, at present only Sentinel – 3 is in orbit, the continuous altimeter based WL monitoring of some of these reservoirs (Gandhi Sagar, Nathsagar, Ranapratap, Ujjani, and Ukai) was attempted through Sentinel-3A satellite data from 2016 to 2018. The accuracy of the retrieved WL was than validated against the observed WL. In most of the reservoirs, a systematic bias was found due to the different characteristics and geoid height of each reservoir. The coefficient of determination, R2, value for a majority of reservoirs was as good as 0.9. In the case of ERS-2, the values of R2 varied for 0.44 to 0.97 with root mean square error (RMSE) in the range of 0.63 to 2.72m. These statistics improved with the ENVISAT RA-2 data analysis, the R2 value reached more than 0.90 for around 11 reservoirs. The highest, 0.99, for Hasdeo and Shivaji Sagar Reservoirs with RMSE of 0.44 and 0.56, respectively. Further, the accuracy improved with the analysis of Saral-AltiKa data. The R2 was always more than 0.9 for each reservoir and the lowest RMSE reduced to 0.03. Therefore, it can be said that the accuracy and consistency of WL retrieval through satellite altimetry has improved with time. Furthermore, the altimeter based retrieved WL may be used in hydrological studies and can contribute to better water resources management.

Data and analysis toolbox for modeling the nexus of food, energy, and water

Energy, water, and food resources are highly interdependent. Agricultural irrigation accounts for 84% of global consumptive freshwater use, the food supply chain demands up to 30% of global primary energy use, and roughly 80% of global electricity generation depends on water for cooling (an average of nearly 100 L of water withdrawn per kWh). Improving understanding of the complex interactions of this resource nexus is, therefore, a top priority for human well-being, sustainable development, and policymaking. Here, we present an interactive analysis toolbox, Nexus of Food, Energy, and Water (NeFEW), that synthesizes available global data to enable modeling and analysis of these resources and their interdependencies at the country-level and for user-specified categories and quantities. Sample analyses also presented here include country-specific estimates of water resources required to produce different types of food and energy, energy required per quantity of water or agricultural product supplied, and CO2-equivalent emissions associated with water and energy provision.

Improved Model Parameter Transferability Method for Hydrological Simulation with SWAT in Ungauged Mountainous Catchments

The sustainability of water resources in mountainous areas has a significant contribution to the stabilization and persistence of the ecological and agriculture systems in arid and semi-arid areas. However, the insufficient understanding of hydrological processes in ungauged mountainous catchments (UMCs) is not able to scientifically support the sustainable management of water resources. The conventional parameter transferability method (transplanting the parameters of the donor catchment model with similar distances or attributes to the target catchment model) still has great potential for improving the accuracy of the hydrological simulation in UMC. In this study, 46 river catchments, with discharge survey stations and multi-type catchment characteristics in Xinjiang, are separated into the target catchments and donor catchments to promote an improved model parameter transferability method (IMPTM). This method synthetically processes the SWAT model parameters based on the distance approximation principle (DAP) and the attribute similarity principle (ASP). The performance of this method is tested in a random gauged catchment and compared with other traditional methods (DAP and ASP). The daily runoff simulation results in the target catchment have relatively low accuracy by both the DAP method ( N S = 0.27, R 2 = 0.55) and ASP method ( N S = 0.36, R 2 = 0.65), which implies the conventional approach is not capable of processing the parameters in the target regions. However, the simulation result by IMPTM is a significant improvement ( N S = 0.69, R 2 = 0.85). Moreover, the IMPTM can accurately catch the flow peak, appearance time, and recession curve. The current study provides a compatible method to overcome the difficulties of hydrological simulation in UMCs in the world and can benefit hydrological forecasting and water resource estimation in mountainous areas.

Role of surface water in the formation of underground water in Aghstafachai-Ganjachai interfluve and its usage prospects

With the purpose of water resources estimation of Aghstafachai-Ganjachai interfluve, geophysical surveys have been carried out. As a result, based on the well data, the maps were developed and interface conditions of the territory specified. The tectonic structure of Aghstafachai-Ganjachai interfluve lowland is associated with the formation of a sole artesian basin on its territory. Aghstafachai, Hasansu, Tovuzchai, Zeyemchai, Shamkirchai, Goshgarchai, Ganjachai and Kurekchai, being the main source of groundwater recharge, created a combined alluvial cone on the territory. The underground water of these rivers were studied in the sloping plain and the estimation of its volume carried out as well. It was revealed that the underground water on a large part of studied territory is fresh and less mineralized. From the bacteriological point of view, it is clean water. The content of microelements and harmful chemical substances in the water does not exceed the standards set for the drinkable water. Despite the long-term usage, the quality of the underground water, the mineralization degree and chemical composition remain unchanged. The regime of underground water is characterized with the irrigated climate type. Hydrogeological indexes and the parameters of ground water horizon in the studied area justify the possibility of their research for water supply.

Relevance of spatio-temporal rainfall variability regarding groundwater management challenges under global change: case study in Doñana (SW Spain)

Rainfall is the major contribution for groundwater recharge in arid and semiarid climates, therefore a key factor in water resources estimation. This work presents the results of an in-depth study in Doñana National Park concerning groundwater recharge behavior over a long period (1975–2016). The spatio-temporal kriging algorithm was used as a supportive tool to improve the reconstruction of the spatio-temporal rainfall variability. One of the main findings was that monthly recharge estimations range between 21 and 91% of the maximum rainfall, being overestimated in areas that also demonstrate spatial heterogeneity in rainfall distribution. In the light of these results, for water management purposes in the Mediterranean area, rainfall spatio-temporal scale is a critical aspect and it must be taken into account in groundwater reservoir allocation. Moreover, it is highlighted that local studies of rainfall and recharge, in an area of high ecological fragility, are essential to developing management strategies that prevent climate change effects and guarantee optimal conditions for groundwater resources in the future.

Demand estimation of water resources via bat algorithm

In the process of urban water resources planning, the demand estimation of urban water consumption is one of the important basic contents. In this paper, a hybrid model of linear estimation model and an exponential estimation model are proposed to forecast the water consumption. The bionic intelligent algorithms are widely used in industrial engineering, so, we use intelligent algorithms to solve the proposed model including Bat Algorithm (BA) and modified Bat Algorithm (FTBA). FTBA improves the global search capability, and the improvements increase the probability of solving the optimal value. In the simulation experiments, we use the data from Nanchang city during 2003 to 2015. The data from 2003 to 2012 are used to find the optimal weights, and the remaining data (2013-2015) are used to test the models. Simulation results show that the modified BA (FTBA) is superior to the standard algorithm and achieves higher accuracy in prediction.

Demand estimation of water resources via bat algorithm

In the process of urban water resources planning, the demand estimation of urban water consumption is one of the important basic contents. In this paper, a hybrid model of linear estimation model and an exponential estimation model are proposed to forecast the water consumption. The bionic intelligent algorithms are widely used in industrial engineering, so, we use intelligent algorithms to solve the proposed model including Bat Algorithm (BA) and modified Bat Algorithm (FTBA). FTBA improves the global search capability, and the improvements increase the probability of solving the optimal value. In the simulation experiments, we use the data from Nanchang city during 2003 to 2015. The data from 2003 to 2012 are used to find the optimal weights, and the remaining data (2013-2015) are used to test the models. Simulation results show that the modified BA (FTBA) is superior to the standard algorithm and achieves higher accuracy in prediction.

Remote control rain sampler for rainfall runoff collection

Abstract The availability of high spatial resolutions rainfall runoff data is vitally important in water resource estimation and management. To settle the problem of untimely collection of rainfall runoff caused by the uncertainty of rainfall, an unattended rainfall runoff collector with remote control was developed. According to the whole system, when the trigger device detects rainfall runoff, an energy-saving circuit connects, and the sampler is powered. At the same time, state and position information are sent to the mobile phone through the communication module. Then, the mobile phone sends instructions and starts an order back to the sampler through texts based on different requirements. The instructions and start order are then passed to the single-chip micyoco (SCM) control system, which also occurs via the communication module. A disconnected signal will be sent to the energy-saving circuit after the rainfall runoff collection. This signal will power off the sampler at the end. Test experiments showed that the maximum sampling error of the sampler developed in this paper was 1′02″, and the minimum sampling error was 22″.

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.