Search Results (16)

In this study, we analyzed the suitability of using the CHIRPS, CMORPH and TRMM platforms in monitoring extreme precipitation events, precipitation–runoff relationships, and seasonal/year-to-year variability in the Saltito semiarid sub-basin in the Mexican state of Durango. Satellite precipitation products (SPP) in 16 sites were contrasted point to point with data from rainfall gauge stations and with a daily temporal resolution for the period of four years (2015–2019). Using this information, we constructed Rx1d, Rx2d, R25mm, and RR95 extreme rainfall indices. For the precipitation–runoff relationships, a runoff model based on the Storm Water Management Model (SWMM) was calibrated and validated with gauge data, and we obtained the Qx1d, Qx2d, and Qx3d runoff indices. We used the bias volume (%), MSE, correlation coefficient, and median bias to evaluate the ability of satellite products to detect and analyze extreme precipitation and run flow events. Although these sensors tend to overestimate both precipitation levels and the occurrence of extreme precipitation events, their high spatial and temporal resolutions make them a reliable tool for the analysis of trends in climate change indices. As a result, they serve as a useful resource in evaluating the intensity of climate change in the region, particularly in terms of precipitation patterns. They also allow hydrological modeling and the observation of precipitation–runoff relationships. This is relevant in the absence of precipitation and hydrometric information, which is usually common in vast regions of the developing world. Full article

High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (<0.4 on average), while the false alarm ratio was significant (>0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations. Full article

In weather radar applications, the Z-R relationship is considered one of the most crucial factors for providing quality quantitative precipitation estimates. However, the relationship’s parameters vary in time and space, making the derivation of an optimal relationship for a specific weather radar system challenging. This research focused on the analysis of the spatiotemporal variability of the parameters for a newly installed X-Band weather radar in Athens, Greece, by performing correlation and optimization analyses between high temporal resolution weather radar and rain gauge datasets. The correlation analysis was performed to assess the available datasets and provide the base of quality control. Multiple Z-R relationships were then derived for the following three optimization procedures; event-based relationships, station-based relationships, and a single area-based relationship. The results highlighted the region’s spatial variability regarding the Z-R relationship and the correlation between the station location and its parameter values. Moreover, it was found that stations near the coast and the front end of precipitation systems featured parameter values typical of convective type events. Finally, a single Z-R relationship was determined under a calibration and validation scheme, Z = 321R^1.53,, which was validated with good agreement. Full article

The Ohrid-Prespa lake system is the oldest and most diverse permanent lake system in Europe, dating from the Pliocene era and aged at over 4Ma. Its smaller component is Lake Macro Prespa (thereafter called Prespa), shared by North Macedonia, Albania, and Greece. Lake Prespa’s depth was reported as 14 m mean and 48 m maximum before its major water level decline. The lake is highly sensitive to external impacts, including climate change, and has been suffering major water loss for decades. A lake-level decline of almost 10 m was documented between 1950 and 2009 due to restricted precipitation and increased water abstraction for irrigation. This study describes the changes in the surface size of Prespa Lake and the vegetation/land use in the surrounding area in the period 1984–2020 using satellite images (remote sensing, Landsat 5 & 8 images by United States Geological Survey). The lake lost 18.87 km² of surface in this period (6.9% of its size, dropping from 273.38 km² to 254.51 km²). Water loss was greater in the period 1987–1993 and 1998–2004. The Analysis of Normalized Difference Vegetation Index (NDVI) in the area (app. 4950 km²) surrounding Lake Prespa revealed an increase in the mean NDVI values over the period studied (1984–2020), pointing to a general increase in vegetation. Areas with NDVI > 0.13 increased from 78% in 1984 to 86% in 2020, while those with the highest vegetation intensity (NDVI > 0.45) increased by 40%. These changes in vegetation may be related to the water loss of the lake. Full article

Wildfires are an important disturbance affecting catchments’ soil and hydrological processes within. Wildfires are predicted to increase in both frequency and severity under climate change. Here, we present measurements of tritium (³H) in surface water of three streams before and after the ‘las Máquinas’ megafire of January 2017 in central Chile and streamflow metrics. Mean transit times (MTTs) of water were calculated in three coastal catchments with the Mediterranean climate type, covered by native forest, a mixture of native forest and Pinus radiata D. Don, and P. radiata. Lumped parameter models (LPMs) were used to obtain MTTs. Tritium activities from 2012 to 2018 ranged from 0.597 to 0.927 Tritium Units (TU), with the lowest TU activity in 2018. These ³H concentrations indicated water ages from 5 to 30 years. Following the fire, peak flows and baseflow have increased in two catchments but decreased in the third. Even though we have seen changes in the hydrological responses within the three catchments, pre- and post-fire MTT values were not significantly different. Therefore, there is no conclusive evidence of hydrological changes at the groundwater level due to wildfire at this early stage. However, since the MTT ranges from 5 to 30 years, it is likely that more time is required for the changes in the hydrograph to be clearly reflected in the tritium signal even though there are noticeable changes in streamflow metrics such as runoff and baseflow. Within the following years from this study, a sampling schedule to continue to investigate both the long-term drought and the effect of wildfire on these catchments will be maintained. Full article

The Natural Park of Albufera (Valencia, Spain) is one of the Spanish Mediterranean wetlands where rice is cultivated intensively. The hydrology of the Albufera Lake, located in the center, combines natural contributions with complex human management. The aim of our study was to develop a new methodology to accurately detect the volume of flood water in complex natural environments which experience significant seasonal changes due to climate and agriculture. The study included 132 Landsat images, covering a 15-year period. The algorithm was adjusted using the NDWI index and simultaneous measurements of water levels in the rice fields. The NDVI index was applied to monitor the cultivated area during the summer. Lake inflows and residence times were also evaluated to quantify how the hydrodynamic of the lake is conditioned by the agricultural management. The algorithm developed is confirmed as a useful ecological tool to monitor the flood cycle of the wetland, being able to detect even the lowest water levels. The flood dynamics are consistent over the fifteen years, being in line with the rice cultivation cycle. Water renewal in Albufera lake is altered with respect to that expected according to the rainfall recorded in the study area, so an improvement in the water management of the hydrological basin is required to optimize the runoff during the rainiest months. Full article

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. Full article

Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks. LSTM-REG covers the period from 1980 to 2016 at a 0.5${}^{°}$ spatial and daily temporal resolution. LSTM-REG has been extensively validated and benchmarked against GR4J-REG, a gridded runoff dataset based on a parsimonious regionalization scheme and the GR4J hydrological model. While both datasets provide runoff estimates with reliable prediction efficiency, LSTM-REG outperforms GR4J-REG for most basins in the independent evaluation set. Thus, the results demonstrate a higher generalization capacity of LSTM-REG than GR4J-REG, which can be attributed to the higher efficiency of the proposed LSTM-based regionalization scheme. The developed datasets are freely available in open repositories to foster further regional hydrology research in northwest Russia. Full article

With the growing concerns of water quality related to tile drainage in agricultural lands, developing an efficient and cost-effective method of mapping tile drainage is essential. This research aimed to establish mapping of tile drainage systems in agricultural fields using optical and radiometric thermal sensors mounted on Unmanned Aerial System (UAS). The overarching hypothesis is that in a tile-drained land, spatial distribution of soil water content is affected by tile lines, therefore, contrasting soil temperature signals exist between areas along the tile lines and between the tile lines. Designated flights were conducted to assess the effectiveness of the UAS under various conditions such as rainfall, crop cover, crop maturity and time of the day. Image correction, mosaicking, image enhancements and map production were conducted using Agisoft and ENVI image analysis software. The results showed intermediate growth stage of soybean plants and rainfall helped delineating tile lines. In-situ soil temperature measurements revealed appropriate time of the day (14:00 to 18:00 h) for thermal image detection of the tile lines. The role of soil moisture and plant cover is not resolved, thus, further refinement of the approach considering these factors is necessary to develop efficient mapping techniques of tile drainage using UAS thermal and optical sensors. Full article

A classic method for assessing the reference crop evapotranspiration (ET_o) is the pan evaporation (E_pan) method that uses E_pan measurements and pan coefficient (k_p) models, which can be functions of relative humidity (RH), wind speed (u₂), and temperature (T). The aim of this study is to present a methodology for evaluating the robustness of regression coefficients associated to climate parameters (RH, u₂, and T) in pan method models during recalibration at local conditions. Two years of daily data from April to October (warm season) of meteorological parameters, E_pan measurements from class A pan evaporimeter and ET_o estimated by ASCE-standardized method for the climatic conditions of Thessaloniki (Greece, semi-arid environment), were used. The regression coefficients of six general nonlinear (NLR) regression E_pan models were analyzed through recalibration using a technique called “random cross-validation nonlinear regression RCV-NLR” that produced 1000 random splits of the initial dataset into calibration and validation sets using a constant proportion (70% and 30%, respectively). The variance of the regression coefficients was analyzed based on the 95% interval of the highest posterior density distribution. NLR models that included coefficients with a 95% HPD interval that fluctuates in both positive and negative values were considered nonrobust. The machine-learning technique of random forests (RF) was also used to build a RF model that includes E_pan, u₂, RH, and T parameters. This model was used as a benchmark for evaluating the predictive accuracy of NLR models but, also, for assessing the relative importance of the predictor climate variables if they were all included in one NLR model. The findings of this study indicated that locally calibrated NLR functions that use only the E_pan parameter presented better results, while the inclusion of additional climate parameters was redundant and led to underfitting. Full article

Parametrising the spatially distributed dynamic catchment water balance is a critical factor in studying the hydrological system responses to climate and land use changes. This study presents the development of a geographic information system (GIS)-based set of algorithms (geographical spatially distributed water balance model (GEO-CWB)), which is developed from integrating physical, statistical, and machine learning models. The GEO-CWB tool has been developed to simulate and predict future spatially distributed dynamic water balance using GIS environment at the catchment scale in response to the future changes in climate variables and land use through a user-friendly interface. The tool helps in bridging the gap in quantifying the high-resolution dynamic water balance components for the large catchments by reducing the computational costs. Also, this paper presents the application and validation of GEO-CWB on the Shannon catchment in Ireland as an example of a large and complicated hydrological system. It can be concluded that climate and land use changes have significant effects on the spatial and temporal patterns of the different water balance components of the catchment. Full article

The aim of this paper is the application of temporal analysis of daily and 10 min of rainfall data from Poprad station, located in Eastern Slovakia. There are two types of data used in the analysis, firstly, a daily time step data, manually collected between the years 1951 and 2018 and secondly, 10 min of data, automatically collected between the years 2000 and 2018. For proper comparability, the automatically collected data has been recalculated to the daily form. After a comparison of the sets of data, manually collected daily data has been used in further analysis. The main analysis can be divided into two sections. The first section consists of basic statistics (mean, standard deviation, etc.) and the second section of descriptive statistics, where the subjects of examination were trend, stationarity, homogeneity, periodicity and noise. The results of the basic statistics outlined trend behavior in the data meaning that the annual total rainfall for the period 1951–2018 is slightly increasing but the further investigation supported by the methods of descriptive statistics refuted this thesis. The number of rainy days is decreasing but maximum rainfall intensity is increasing year by year, indicating that total rainfall is happening in lesser and lesser days, with an increase in the number of 0 rainfall days. The results demonstrated no presence of the trend or only a weak trend in daily time step, but a significant increasing trend in annual rainfall. Tests of stationarity proved that the data are stationary and, therefore, suitable for any hydrologic analysis. The tests of homogeneity showed no breakpoints in the data. The interesting result was demonstrated by the periodicity test, which showed exactly a 365.25 days’ period, while 0.25 indicates a leap year. As a summary for the Poprad station, there is no tendency of increasing of daily average rainfall, but slight increasing trend of total annual rainfall, the summer season has the highest ratio on total precipitation per year, September and October are the months with the highest numbers of days without rain. Full article

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management. Full article

Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12. Full article

Variability and covariability of land properties (soil, vegetation and subsurface geology) and remotely sensed soil moisture over the southeast and south-central U.S. are assessed. The goal is to determine whether satellite soil moisture memory contains information regarding land properties, especially the distribution karst formations below the active soil column that have a bearing on land-atmosphere feedbacks. Local (within a few tens of km) statistics of land states and soil moisture are considered to minimize the impact of climatic variations, and the local statistics are then correlated across the domain to illuminate significant relationships. There is a clear correspondence between soil moisture memory and many land properties including karst distribution. This has implications for distributed land surface modeling, which has not considered preferential water flows through geologic formations. All correspondences are found to be strongest during spring and fall, and weak during summer, when atmospheric moisture demand appears to dominate soil moisture variability. While there are significant relationships between remotely-sensed soil moisture variability and land properties, it will be a challenge to use satellite data for terrestrial parameter estimation as there is often a great deal of correlation among soil, vegetation and karst property distributions. Full article