Study Of Hydrological Processes Research Articles

Quantifying the Atmospheric Water Balance Closure over Mainland China Using Ground-Based, Satellite, and Reanalysis Datasets

Quantifying the atmospheric water balance is critical for the study of hydrological processes in significant regions. This study quantified atmospheric water balance closure at 205 stations in mainland China on a monthly timescale from 2009 to 2018 using datasets from ground- and satellite-based observations and reanalysis data. The closure performances were firstly quantified using the mean and root mean square (RMS) of the residuals, and the possible influencing factors were explored, as well as the influence of different water balance components (WBCs) using different datasets. In the closure experiment using ERA5, the mean and residuals were 6.26 and 12.39 mm/month, respectively, on average, which indicated a closure uncertainty of 12.8%. Using ERA5 analysis as a reference, the closure experiment using different combinations revealed average mean residuals of 8.73, 11.50, and 15.89 mm/month, indicating a precipitation closure uncertainty of 22.0, 23.7, and 24.4% for the ground- and satellite-based observations and reanalysis data, respectively. Two possible influencing factors, station latitude and the climatic zone in which the station is located, were shown to be related to closure performance. Finally, the analysis of the impact from different WBCs showed that precipitation tended to have the most significant impact, which may have been due to larger observation uncertainties. Generally, the atmospheric water balance in mainland China can be closed using datasets from different observational techniques.

ГИДРОЛОГИЧЕСКАЯ НАУКА И ПРАКТИКА XXI ВЕКА

The main problems of modern hydrology that arise in the study of hydrological processes and in the scientific support of the design and operation of hydraulic structures, planning and implementation of measures for the use and protection of water resources, protection of the population and economic facilities from hazardous hydrological phenomena are considered. The main directions for solving these problems are formulated, providing for increasing the volume and improving the quality of hydrometeorological information, improving the technical means of its processing and analysis, and developing the theoretical foundations for a quantitative description of the processes occurring in water bodies on land and in their catchments. These directions are relevant for our country and are recognized by the international hydrometeorological community.

Investigating Soil Pore Network Connectivity in Varied Vegetation Types Using X-ray Tomography

The ecological environment in southwestern China is fragile. Due to the significant preferential flow in vertical and horizontal directions and poor water conservation ability, vegetation degradation still exists under conditions of abundant rainfall. Therefore, the pore connectivity and infiltration characteristics in shallow soil under typical local vegetation need to be studied. A calculation model for the vertical connectivity of soil macropores was independently constructed, and differences in soil macropore structures and the degree of vertical connectivity in typical vegetation types (natural secondary forest, natural grassland, Yunnan pine plantation, eucalyptus plantation, cypress plantation, mulberry bushes) were investigated by CT scanning technology of undisturbed soil columns. The results showed that the vertical connectivity of large pores in the shallow soil of the region can be quantitatively described by X-ray tomography, and the total surface area and cumulative curvature of macropores in natural grassland soil were two or three times that in artificial vegetation. The concentration area of macropores in the soil of artificial forestland was closer to the surface, and the tendency of macropore preferred path decreased by 76.18% around 30 cm depth in the soil. The vertical connection of soil macropores in artificial forests was significantly lower than that of natural secondary forestlands (33.03%) and natural grasslands (36.75%). The restoration of the plantation improved surface soil pore structure, and the vertical connectivity of soil is nearly 20% less than that of natural vegetation types (natural secondary forestland, natural grassland), which reduced water outflow rate by nearly 44% and electrolyte content by nearly 14% at a depth of 30 cm. This study provided data and research directions for the study of hydrological processes in local forest vegetation and technical support for solving the problems of soil water loss and forestland water conservation in southwestern China.

A numerical study of hydrodynamic characteristics and hydrological processes in the coastal wetlands during extreme events

A numerical study of hydrodynamic characteristics and hydrological processes in the coastal wetlands during extreme events

Transformation Characteristics and Mechanism of Blue and Green Water flows at Watershed and Typical Ecosystem Scale in China

The transformation of blue and green water is an important hydrological process at the watershed, which is an important supplement to the study of hydrological processes. The Soil and Water Assessment Tool (SWAT) was used to investigate the evolution characteristics of hydrological processes and the transformation mechanism at the watershed and ecosystem scales in the Dongjiang River. The results show that: (1) the annual average total water flows in the Dongjiang River Basin are 37.80 and 22.287 billion m³, respectively. The blue and green water flows during the wet season account for more than 75% of the total year. The blue and green flows of the grassland ecosystem increased significantly, with the most significant change in the downstream ecosystem. (2) From 1980 to 1990 and from 2010 to 2020, the Dongjiang River Basin experienced a transition from blue to green water flow. Blue water flow accounts for the majority of total water flow in the paddy field and dry cropland ecosystems, and green water flow changed to blue water flow from 1980 to 2010. (3) The correlation between blue and green flow and land use was weak throughout the year and during the wet season, but it was significant during the dry season. This study can serve as a model for the integrated management of water resources and ecosystems in the Dongjiang River Basin.

Quantitative Evaluation of the Impact of Vegetation Restoration and Climate Variation on Runoff Attenuation in the Luan River Basin Based on the Extended Budyko Model

The occurrence of water scarcity and extreme hydrological events is becoming increasingly frequent worldwide. The river runoff process, as an important link in the water cycle of a basin, is an essential content of the study of hydrological processes in the basin. Vegetation is an influencing factor closely related to hydrological processes in a watershed. This article quantitatively analyzes the impact of vegetation restoration and climate variation on runoff in the Luan River Basin from 1982 to 2018. Firstly, trend analysis was conducted on runoff depth, precipitation, the Normalized Vegetation Index (NDVI), and reference evaporation. Then, Mann–Kendall mutation analysis and Pettitt mutation analysis were used to identify the year of runoff mutation. Finally, a Budyko model was constructed to quantitatively analyze the impact of vegetation and climate change on the runoff of the Luan River. The results showed that: (1) The runoff reduction rate of the Luan River was 1.2437 mm/a. The precipitation was increasing at a rate of 0.6977 mm/a. The reference evaporation rate decreased at a rate of 0.0977 mm/a. The annual growth rate of the Budyko parameter (n) was 0.0283. The annual growth rate of NDVI was 0.0022. (2) The coefficient of determination in the functional equation (a = 14.74, b = −3.4105) for NDVI and Budyko parameter (n) was 0.3297 (p < 0.01). (3) The contributions of Pr, ET0, NDVI, and (n) to reduced flow were −17.49%, 1.46%, 35.25%, and 80.78%, respectively. The increase in vegetation would lead to a decrease in runoff. This study can clarify the impact of vegetation restoration on water resource security in the Luan River Basin.

Intercomparison of snow water equivalent products in the Sierra Nevada California using airborne snow observatory data and ground observations

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

Preferential Flow and Preferential Path Characteristics of the Typical Forests in the Karst Region of Southwest China

The ecological environment in the karst region of southwest China is fragile, and vegetation degradation is considered the main cause of this phenomenon. Vegetation growth is affected by preferential flow due to a large number of fissures and macropores in the soil. Therefore, the preferential flow and preferential paths in local typical vegetation need to be studied. The distribution and developmental characteristics of preferential flow and preferential paths in three typical local stands were obtained by conducting field staining tracer experiments and using image processing techniques. The results showed that there was preferential flow movement in the soil moisture of the local understorey and that there was a high degree of preferential flow development and high variability. The degree of preferential flow in several typical forests can be expressed, from highest to lowest, in the following order: secondary forests, Cupressus duclouxiana Hickel (Hickel., 1814) forest, and Pinus massoniana (Lamb., 1803) forest. The preferential paths in the soil were mainly distributed above 15 cm, and the number of preferential paths with a small influence radius was much greater than that with a large influence radius. The preferential paths with an influence radius of 2.5~5 mm accounted for the largest proportion. The total number of preferential paths in the P. massoniana forest was small. The preferential paths in the secondary and C. duclouxiana Hickel forests were similar in number, but the secondary forests had more preferential paths with a large influence radius. This study investigated the characteristics of preferential flow and preferential paths in the typical forests of the karst region in southwest China, and it can provide data and research directions for the study of hydrological processes in local forest vegetation.

Variations of Stable Isotopes in Daily Precipitation in a Monsoon Region

The stable isotopes of hydrogen and oxygen in precipitation provide a useful reference for the study of hydrological processes, but concerns have been raised regarding the established patterns in their variations in a monsoon climate zone. In this study, stable isotopes (δ18O and δ2H) of 539 daily precipitation samples from seven hydrometeorological stations in the Beijing area were used to investigate short-term isotopic variations and the controlling factors at a region with a monsoon climate. The δ18O in the precipitation increases from the northwest to the south, which is controlled by the monsoon from the south and continental moisture from the northwest. Consistently, both the high δ18O values and low deuterium excess values from May to September reveal the impact of the monsoon. The amount effect is significant during the monsoon season. In contrast, the T effect is significant, with a gradient of 0.4‰ per °C during the non-monsoon season. A Rayleigh distillation model indicates that the moisture source and residual vapor fraction are the two most important factors in controlling the δ18O patterns in precipitation in a monsoon region, independent of temperature. The result of this study is helpful for the understanding of the regional atmospheric water cycle.

Simulating the water δ18O of a small open lake in the East Asian monsoon region based on hydrologic and isotope mass-balance models

• The systematic observations benefit the lake water isotopes (δ L ) simulations. • This study well simulated the temporal variations of δ L of a small and open lake. • The MBM captured the influences of extreme weather events on the δ L . • The sensitivity test well evaluated the influences of weather seasonality on the δ L . Hydrologic and isotope mass-balance models (MBMs) are useful tools for the simulation and quantitative interpretation of lake water δ 18 O (δ L ). Such studies of small, open lakes are important because δ L may show large responses to weather-driven hydrological variability. A δ L sequence was simulated for three-year and five-day interval data from Taozi Lake, a small, open lake in Changsha, in the East Asian monsoon region of China. The MBMs performed well, with the optimal model explaining 90% of the observed δ L variability. However, it was necessary to parameterize the precipitation input by increasing the lake water depth during heavy precipitation events to capture the observed sharp decreases in δ L . We found that the MBMs using air temperature and the equilibrated atmospheric vapor δ 18 O (δ A ) produced a more negative δ L compared to the model using the surface water temperature and the observed δ A , and that the equilibrated δ A was somewhat arbitrary, which introduced a large error in the model output. A sensitivity test highlighted the importance of the seasonal variability of hydrometeorological and isotopic variables to reproduce the observed δ L variability. Additionally, the simulated average δ L showed a similar pattern of variation to the meteorological variables shifted by different ratios, but for the relative humidity, there was only a narrow window within which a realistic δ L series could be reproduced. Our results show that a realistic MBM can be established based on long-term observations, with implications for studies of hydrologic processes and paleoclimate reconstruction.

Experimental studies of hydrological processes in sea estuaries using CTD probe

Experimental studies of hydrological processes in sea estuaries using CTD probe

Remote sensing of atmospheric HDO/H2O in southern California from CLARS-FTS

Atmospheric isotopologues of water vapor (e.g., HDO) are important tracers for understanding Earth's hydrological cycles. Most remote sensing and in-situ measurements of these isotopologues, however, are either column averaged values or sparse in space and time. Measurements targeting the planetary boundary layer (PBL), the part of the atmosphere that has high sensitivity to surface sources of water vapor isotopologues, are much rarer. In this study, we retrieved HDO and H2O columns from observations by the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS), a mountaintop observatory on Mt. Wilson (1.67 km a.s.l.) overlooking the Los Angeles (LA) basin in southern California. CLARS-FTS observations are highly sensitive to the lower atmosphere due to the long light path along the PBL. Retrievals were conducted using spectral windows between 6000 and 7000 cm−1 from CLARS-FTS observations (2011–2019). The isotopological abundance δD, which represents the relative difference of the HDO/H2O ratio to a standard abundance ratio, is also calculated. The averaged δD retrievals are (−156.1 ± 60.0)‰ with an uncertainty of (6.1 ± 10.2)‰ for LA Basin Survey mode and (−344.7 ± 95.0)‰ with an uncertainty of (42.4 ± 31.6)‰ for Spectralon Viewing Observation mode. In LA, the δD shows a seasonal cycle that is primarily driven by the change of atmospheric humidity. A comparison analysis shows that the δD measurements by CLARS-FTS, a collocated Total Carbon Column Observing Network (TCCON), and the TROPOspheric Monitoring Instrument (TROPOMI) are in good agreement. The difference between CLARS and TCCON δD retrievals can primarily be attributed to the difference in their observation geometries. We envision that the HDO and δD measurements from CLARS-FTS provide high spatial and temporal resolution datasets for further study of hydrological processes, such as the partitioning of the water flux into soil evaporation and transpiration, standing water evaporation, or transport and mixing from the oceans, in the LA megacity.

Studies on Hydrological Processes on Karst Slopes for Control of Soil and Water Loss

Soil and water loss in karst areas seriously restricts the sustainable development of karst ecosystems, the economy and society in southwest China, which has been a concern of, and studied by, many scholars in China and abroad. Soil and water loss has a great influence on the evolution of rocky desertification, groundwater quality, drought and flood disasters in karst regions. This paper aimed to provide a review of studies of slope hydrological processes in soil and water loss. In this paper, 322 related articles retrieved from the Web of Science database and CNKI database were systematically reviewed. Firstly, a quantitative study was conducted to analyze the annual number, countries and research progress of the published literature. Secondly, the main progress and achievements of slope hydrology and soil erosion control technology were classified and summarized according to theoretical research, mechanism research, technology research and technical demonstration. Finally, the key problems and future research prospects are put forward, starting with the existing technology, in order to find more suitable soil and water loss control measures in karst regions and achieve economic and ecological benefits.

A Cascaded Spectral–Spatial CNN Model for Super-Resolution River Mapping With MODIS Imagery

Rivers are important elements of the earth’s ecosystem, and their spatial distribution information is critical for the study of hydrological and biogeochemical processes. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery has been widely used for river mapping due to its high temporal resolution and long-term observation records, which are essential to capture the rapid fluctuation of rivers. However, when the conventional hard classification methods are used, the accuracy of the river maps produced from MODIS data (and especially for those rivers with narrow widths) is often limited because mixed pixels are common in MODIS imagery, due to coarse spatial resolution. In this article, a cascaded spectral–spatial information combined deep convolutional neural network (CNN) model for super-resolution river mapping (DeepRivSRM) is proposed to produce Landsat-like fine-resolution river maps from MODIS images. In DeepRivSRM, a CNN made up of a spectral unmixing module and a super-resolution mapping (SRM) module is introduced to handle the spectral and spatial information simultaneously. Moreover, for training the DeepRivSRM model, an adaptive cross-entropy loss function incorporating the fraction information of the rivers is designed to improve the performance of the DeepRivSRM model for small rivers. The proposed method was evaluated with MODIS images from three test sites and was compared with hard classification, a conventional SRM method, and a CNN-based SRM method. The results show that DeepRivSRM can generate more accurate river maps by effectively learning the subpixel-scale spatial–spectral information in MODIS imagery.

Formation factors of surface inflow to antarctic lakes of the Larsemann Hills oasis

The study aims to identify formation factors of water inflow to the Antarctic lakes of the Larsemann Hills oasis (East Antarctica). The objects of study are 11 lakes of the oasis. The analysis was performed based on the expeditionary data of the Russian Antarctic Expedition (RAE): 63rd season (23 December 2017 – 3 February 2018), 64th season (12 January 2019 – 27 February 2019), 65th season (2 November 2019 – 24 March 2020). Data of lakes water level observations, aerial photography of the unmanned aerial vehicle (UAV) and route surveys are given, the results of identifying the boundaries of the lakes catchments are presented. The factors that determine the formation of water inflow to the lakes in this region were identified based on the analysis of the materials. The most significant are the meteorological conditions, the presence of perennial snowfields and glacial areas in the catchments, and the presence of lakes that can cause outburst flood. The seasonally thawed layer also has an impact on the formation of the inflow to the lakes. The vegetation cover is not so important for inflow formation in this region due to the physical and geographical conditions. As for anthropogenic activity, it mainly affects the environmental situation of the catchments and water quality, while the anthropogenic influence on the formation of water inflow to the lakes in the oasis is limited to the territories of polar stations. The factors identified should be taken into account in the further study of hydrological processes, the creation of models that describe them, and the organization of field observations.

Canal surface evaporation along the China's South-to-North Water Diversion quantified by water isotopes

Water stable isotopes have extensive applications in the study of riverine hydrological processes, in particular, isotopic enrichment occurring along flow direction can be used as an indicator to estimate river surface evaporation. However, this application is difficult in natural rivers due largely to complex water exchange along river channel. China's South-to-North Water Diversion Project (SNWDP) is the largest artificial river designed to divert water from south to north through its enclosed, long span open canal, therefore providing a practice to estimate river surface evaporation by using isotope method. In this paper, we carried out hydrometeorological surveys and sampled canal water along the Middle Route of SNWDP (MRP) in two seasons, July 2018 and April 2019, for δ18O and δ2H measurement, then simulated the isotopic enrichment in canal water by using Craig-Gordon (C-G) evaporation model. We found clear increasing trend in heavy isotopes along the long span canal from head water to the end, resulting from evaporation enrichment. We used C-G evaporation model to estimate evaporation ratio E/V of canal water, and results show a ratio of evaporation loss of 2.54%~3.73% in July 2018 and 1.66%~2.39% in April 2019. We also found obvious seasonal differences existed in canal water isotopes, evaporation enrichment and the CWEL (canal water evaporation line), in association with more intensive evaporation in summer. Some large isotope fluctuations along the canal are mostly related to rainfall events, altering the canal water isotope signal. Our result in this study highlights the potential for water isotopes in the application of inter-basin water resources management, in particular, with increasing stress from water shortage and anthropogenic impact.

Исследование формирования и распространения речного плюма Дуная на основе численного моделирования

The relevance of the studied circulation caused by the water river runoff deals with the anthropological impact on the ecological state of the shelf. River waters, entering into the sea, form mesoscale structures in the delta’s area, characterized by low salinity with a high level of suspended matter and dissolved organic matter. Such structures are called “plume” in modern literature. In this case, the inertial motion of the plume is free to form a rounded area or “bulge” like anticyclonic circulation type of flow. The purpose of this work is to investigate the propagation of freshened waters, formed by the river inflow, the formation of the waters hydrological structure regularities, dynamics of the buoyancy current, and thermohaline front formation on the base of numerical modeling. Numerical modeling is used to study the formation of a river plume and downward propagation of buoyancy current on the shelf without taking into account the tides forcing. A three-dimensional σ-coordinate numerical model was used, adapted for the shelf and estuaries. The calculations were carried out for a rectangular box area. The influence of changes in the main parameters of the runoff, the mouth geometry and wind forcing are considered to development of the plume and the alongshore propagation of buoyancy current. The obtained modeling results for the conditions of the Northwestern Black Sea shelf and the Danube discharge are consistent with the estimates of plume characteristics based on the archival hydrological observations data of water temperature and salinity. The results of this work can be used for further study of hydrological processes in the region of river mouths, the peculiarities of the plumes formation and evolution, assessment of the suspended matter, biogenic elements and microplastics transport in the sea and ocean coastal zones.

Using InSAR to identify hydrological connectivity and barriers in a highly fragmented wetland

AbstractHydrological connectivity is a critical determinant of wetland functions and health, especially in wetlands that have been heavily fragmented and regulated by human activities. However, investigating hydrological connectivity in these wetlands is challenging due to the costs of high‐resolution and large‐scale monitoring required in order to identify hydrological barriers within the wetlands. To overcome this challenge, we here propose an interferometric synthetic aperture radar (InSAR)‐based methodology to map hydrologic connectivity and identify hydrological barriers in fragmented wetlands. This methodology was applied along 70 transects across the Baiyangdian, the largest freshwater wetland in northern China, using Sentinel 1A and 1B data, covering the period 2016–2019. We generated 58 interferograms providing information on relative water level changes across the transects that showed the high coherence needed for the assessment of hydrological connectivity. We mapped the permanent and conditional (temporary) barriers affecting connectivity. In total, 11% of all transects are permanently disconnected by hydrological barriers across all interferograms and 58% of the transects are conditionally disconnected. Areas covered by reed grasslands show the most undisturbed hydrological connectivity while some of these barriers are the result of ditches and channels within the wetland and low water levels during different periods of the year. This study highlights the potential of the application of Wetland InSAR to determine hydrological connectivity and location of hydrological barriers in highly fragmented wetlands, and facilitates the study of hydrological processes from large spatial scales and long‐time scales using remote sensing technique.

The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network

AbstractRiver wetted width (RWW) is an important variable in the study of river hydrological and biogeochemical processes. Presently, RWW is often measured from remotely sensed imagery, and the accuracy of RWW estimation is typically low when coarse spatial resolution imagery is used because river boundaries often run through pixels that represent a region that is a mixture of water and land. Thus, when conventional hard classification methods are used in the estimation of RWW, the mixed pixel problem can become a large source of error. To address this problem, this paper proposes a novel approach to measure RWW at the subpixel scale. Spectral unmixing is first applied to the imagery to obtain a water fraction image that indicates the proportional coverage of water in image pixels. A fine spatial resolution river map from which RWW may be estimated is then produced from the water fraction image by superresolution mapping (SRM). In the SRM analysis, a deep convolutional neural network is used to eliminate the negative effects of water fraction errors and reconstruct the geographical distribution of water. The proposed approach is assessed in two experiments, with the results demonstrating that the convolutional neural network‐based SRM model can effectively estimate subpixel scale details of rivers and that the accuracy of RWW estimation is substantially higher than that obtained from the use of a conventional hard image classification. The improvement shows that the proposed method has great potential to derive more accurate RWW values from remotely sensed imagery.