Assessment of Satellite Rainfall Estimates as a Pre-Analysis for Water Environment Analytical Tools: A Case Study for Tonle Sap Lake in Cambodia

Tonle Sap Lake is the second main source of water supply and food security in Cambodia. However, this area is in the need for rainfall information which can cover the entire area for an accurate hydro-hydraulic modeling, climate modeling and other types of water or environment related modeling. In this case, Satellite Rainfall Estimates (SREs) would play a major role by filling out missing data where gauge observation is not available. The study aims to assess the spatio-temporal performance of two high resolution satellite products such as TRMM 3B42V7 and CHIRPS V.2. One-hundred and fifty four (154) stations around the Tonle Sap Lake and some close to the Mekong River were selected for the analysis within the study period of 2000 to 2004. After this, proper bias correction method is proposed. To do this, GIS and statistical indicators were used for the comparison. Both TRMM and CHIRPS provide a good correlation with the gauge. Around 90% of stations have CC varies from 0.5 to 0.9. In addition, the median bias of SREs are about 30 mm/month. Both satellite showed very similar pattern of bias spatially and temporally. This can be said that even though TRMM has the lower spatial resolution compared to CHIRPS, the performance of it is better. Moreover, TRMM have higher correlation when each of its cells was compared with the averaging of all stations within that cell. 25% of data that have extreme bias ratio maybe due to other underlying factors such as the distance from the station to the city, the soil elevation, landuse type, age of instrument, occurring of storm or drought that need to be taken into account for the further study

Evaluation of satellite-based products for extreme rainfall estimations in the eastern coastal areas of China

Remotely sensed rainfall plays an important role in providing efficient approaches for global or regional rainfall analysis. However, the accuracy of satellite-based products is mainly affected by the errors in sensor observation and retrieval algorithms, particularly with respect to extreme rainfall estimates. The objective of this study is to evaluate the accuracy of satellite-based products in capturing rainfall extremes. The eastern coastal areas of China were chosen as the case study area to compare the accuracy of three mainstream satellite-based products with respect to extreme rainfall estimates during 2003–2015 period. This included the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7, the Climate Prediction Centre Morphing technique RAW (CMORPH-RAW), and the CMORPH bias-corrected product (CMORPH-CRT). In general, all satellite-based products demonstrated numerous errors in extreme rainfall estimates. Based on three different indices of extreme rainfall, it was observed that the satellite-based products underestimated the amounts of rainfall extremes and their respective average values. It was noted that CMORPH-RAW demonstrated the largest relative bias (RB) and underestimated the average extreme rainfall by −31% to −35%. Additionally, all satellite-based products exhibited poor capabilities in capturing the variations in hourly extreme rainfall processes. Finally, a simple potential flood index was developed to simulate the potential flood areas in the eastern coastal areas of China. We found that the potential flood areas can be simulated by combining the potential flood index with the amounts of rainfall derived by satellite-based products

Regional equivalent water thickness modeling from remote sensing across a tree cover/lai gradient in mediterranean forests of northern Tunisia

The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and moisture vegetation indexes for vegetation water content expressed by the equivalent water thickness (EWT) in a Mediterranean forest ecosystem with contrasted water budgets and desiccation rates. We proposed a revised EWT at canopy level (EWTCAN) based on weekly field measurements of fuel moisture in seven species during the 2010 dry period, considering the mixture of plant functional types for water use (trees, shrubs and herbaceous layers) and a varying vegetation cover. MODIS vegetation indexes computed and smoothed over the dry season were highly correlated with the EWTCAN. The performances of moisture indexes Normalized Difference Infrared Index (NDII6 and NDII7) and Global Moisture Vegetation Index (GVMI6 and GVMI7) were comparable, whereas for biomass vegetation indexes, Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Adjusted Normalized Difference Vegetation Index (ANDVI) performed better than Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). We also identified the effect of Leaf Area Index (LAI) on EWTCAN monitoring at the regional scale under the tree cover/LAI gradient of the region from relatively dense to open forest. Statistical analysis revealed a significant decreasing linear relationship; indicating that for LAI less than two, the greater the LAI, the less responsive are the vegetation indexes to changes in EWTCAN; whereas for higher LAI, its influence becomes less significant and was not considered in the inversion models based on vegetation indexes. The EWTCAN time-course from LAI-adapted inversion models based on significantly-related vegetation indexes to EWTCAN showed close profiles resulting from the inversion models using NDVI, ANDVI, MSAVI and NDII6 applied during the dry season. The developed EWTCAN model from MODIS vegetation indexes for the study region was finally tested for its ability to capture the topo-climatic effects on the seasonal and the spatial patterns of desiccation/rewetting for keystone periods of Mediterranean vegetation functioning. Implications for further use in scientific developments or management are discussed

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coeffcient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates

Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region

This paper proposes a protocol to assess the space–time consistency of 12 satellite-based precipitation products (SPPs) according to various indicators, including (i) direct comparison of SPPs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPPs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPPs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the space–time variability of the SPPs and to a large dataset composed of 12 SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TMPA–RT v.7, TMPA–Adj v.7 and SM2Rain–CCI v.2), an unprecedented comparison. The aim of using different space scales and timescales and indicators was to evaluate whether the efficiency of SPPs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPPs. Compared to precipitation gauge observations, some SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, GSMaP v.6, PERSIANN, and TMPA–RT v.7) are unable to estimate regional precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH–BLD v.1, MSWEP v.2.1, PERSIANN–CDR, and TMPA–Adj v.7) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In 9 out of 10 of the cases studied, streamflow was more realistically simulated when SPPs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks, whereas the SPP's ability to reproduce the duration of MODIS-based snow cover resulted in poorer simulations than simulation using available precipitation gauges. Interestingly, the potential of the SPPs varied significantly when they were used to reproduce gauge precipitation estimates, streamflow observations or snow cover duration and depending on the time window considered. SPPs thus produce space–time errors that cannot be assessed when a single indicator and/or time window is used, underlining the importance of carefully considering their space–time consistency before using them for hydro-climatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the highest space–time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration.</p

A combined modelling approach for simulating channel–wetland exchanges in large African river basins

In Africa, many large and extensive wetlands are hydrologically connected to rivers, and their environmental integrity, as well as their influence on downstream flow regimes, depends on the prevailing channel–wetland exchange processes. These processes are inherently complex and vary spatially and temporally. Understanding channel–wetland exchanges is therefore, indispensable for the effective management of wetlands and the associated river basins. However, this information is limited in most of the river basins containing large wetlands in Africa. Furthermore, it is important to understand the links between upstream and downstream flow regimes and the wetland dynamics themselves, specifically where there are water resource developments that may affect these links (upstream developments), or be affected by them (downstream developments). Hydrological modelling of the entire basin using basin-scale models that include wetland components in their structures can be used to provide the information required to manage water resources in such basins. However, the level of detail of wetland processes included in many basin-scale models is typically very low and the lack of understanding of the wetland dynamics makes it difficult to quantify the relevant parameters. Detailed hydraulic models represent the channel-wetland exchanges in a much more explicit manner, but require relatively more data and time resources to establish than coarser scale hydrological models. The main objective of this study was, therefore, to investigate the use of a detailed hydraulic wetland model to provide a better understanding of channel–wetland exchanges and wetland dynamics, and to use the results to improve the parameterisation of a basin-scale model. The study focused on improving the water resource assessments modelling of three data-scarce African river basins that contain large wetlands: the floodplains of the Luangwa and Upper Zambezi River basins and the Usangu wetland in the Upper Great Ruaha River basin. The overall objective was achieved through a combined modelling approach that uses a detailed high-resolution LISFLOOD-FP hydraulic model to inform the structure and parameters of the GW Pitman monthly hydrological model. The results from the LISFLOOD-FP were used to improve the understanding of the channel–wetland exchange dynamics and to establish the wetland parameters required in the GW Pitman model. While some wetland parameters were directly quantified from the LISFLOOD-FP model results, others, which are highly empirical, were estimated by manually calibrating the GW Pitman wetland sub-model implemented in excel spreadsheets containing the LISFLOOD-FP model results. Finally, the GW Pitman model with the inclusion of the estimated wetland parameters was applied for each basin and the results compared to the available downstream observed flow data. The two models have been successfully applied in southern Africa, with the GW Pitman model being one of the most widely applied hydrological models in this region. To address the issue of data scarcity, during setup of these models, the study mainly relied on the global datasets which clearly adds to the overall uncertainty of the modelling approach. However, this is a typical situation for most of the data scarce regions of the continent. A number of challenges were, however, faced during the setup of the LISFLOOD-FP, mainly due to the limitations of the data inputs. Some of the LISFLOOD-FP data inputs include boundary conditions (upstream and downstream), channel cross-sections and wetland topography. In the absence of observed daily flows to quantify the wetland upstream boundary conditions, monthly flow volumes simulated using the GW Pitman monthly model (without including the wetland sub-model) were disaggregated into daily flows using a disaggregation sub-model. The simulated wetland inflows were evaluated using the observed flow data for downstream gauging stations that include the wetland effects. The results highlighted that it is important to understand the possible impacts of each wetland on the downstream flow regime during the evaluations of the model simulation results. Although the disaggregation approach cannot be validated due to a lack of observed data, it at least enables the simulated monthly flows to be used in the daily time step hydraulic model. One of the recommendations is that improvements are required in gauging station networks to provide more observed information for the main river and the larger tributary inflows into these large and important wetland systems. Even a limited amount of newly observed data would be helpful to reduce some of the uncertainties in the combined modelling approach. The SRTM 90 m DEM (used to represent wetland topography) was filtered to reduce local variations and noise effects (mainly vegetation bias), but there were some pixels that falsely affect the inundation results, and the recently released vegetation-corrected DEMs are suggested to improve the simulation results. Channel cross-section values derived from global datasets should be examined because some widths estimated from the Andreadis et al. (2013) dataset were found to be over-generalised and did not reflect widths measured using high-resolution Google Earth in many places. There is an indication that channel cross-sections digitised from Google Earth images can be successfully used in the model setup except in densely vegetated swamps where the values are difficult to estimate, and in such situations, field measured cross-section data are required. Small channels such as those found in the Usangu wetland could play major role in the exchange dynamics, but digitising them all was not straightforward and only key ones were included in the model setup. Clearly, this inevitably introduced uncertainties in the simulated results, and future studies should consider applying methods that simplify extractions of most of these channels from high-resolution images to improve the simulated results. The study demonstrated that the wetland and channel physical characteristics, as well as the seasonal flow magnitude, largely influence the channel–wetland exchanges and wetland dynamics. The inundation results indicated that the area–storage and storage–inflow relationships form hysteretic curves, but the shape of these curves vary with flood magnitude and wetland type. Anticlockwise hysteresis curves were observed in both relationships for the floodplains (Luangwa and Barotse), whereas there appears to be no dominant curve type for the Usangu wetlands. The lack of well-defined hysteretic relationships in the Usangu could be related to some of the difficulties (and resulting uncertainties) that were experienced in setting up the model for this wetland. The storage–inflow relationships in all wetlands have quite complex rising limbs due to multiple flow peaks during the main wet season. The largest inundation area and storage volume for the Barotse and Usangu wetlands occurred after the peak discharge of the wet season, a result that is clearly related to the degree of connectivity between the main channel and those areas of the wetlands that are furthest away from the channel. Hysteresis effects were found to increase with an increase in flood magnitudes and temporal variations in the wetland inflows. Overall, hysteresis behaviour is common in large wetlands and it is recommended that hysteresis curves should be reflected in basin-scale modelling of large river basins with substantial wetland areas. At a daily time scale, inflow–outflow relationships showed a significant peak reduction and a delayed time to peak of several weeks in the Barotse and Usangu wetlands, whereas the attenuation effects of the Luangwa floodplain are minimal. To a large extent, the LISFLOOD-FP results provided useful information to establish wetland parameters and assess the structure of Pitman wetland sub-model. The simple spreadsheet used to estimate wetland parameters did not account for the wetland water transfers from the upstream to the next section downstream (the condition that is included in the LISFLOOD-FP model) for the case when the wetlands were distributed across more than one sub-basin. It is recommended that a method that allows for the upstream wetland inflows and the channel inflows should be included in the spreadsheet. The same is true to the Pitman model structure, and a downstream transfer of water can be modelled through return flows to the channel. The structure of the wetland sub-model was modified to allow an option for the return flows to occur at any time during the simulation period to provide for types of wetlands (e.g. the Luangwa) where spills from the channel and drainage back to the channel occur simultaneously. The setup of the GW Pitman model with the inclusion of wetland parameters improved the simulation results. However, the results for the Usangu wetlands were not very satisfactory and the collection of additional field data related to exchange dynamics is recommended to achieve improvements. The impacts of the Luangwa floodplain on the flow regime of the Luangwa River are very small at the monthly time scale, whereas the Barotse floodplain system and the Usangu wetlands extensively regulate flows of the Zambezi River and the Great Ruaha River, respectively. The results highlighted the possibilities of regionalising some wetland parameters using an understanding of wetland physical characteristics and their water exchange dynamics. However, some parameters remain difficult to quantify in the absence of site-specific information about the water exchange dynamics. The overall conclusion is that the approach implemented in this study presents an important step towards the improvements of water resource assessments modelling for research and practical purposes in data-scarce river basins. This approach is not restricted to the two used models, as it can be applied using different model combinations to achieve similar study purpose